Optimization of a multi-channel system using a feedback loop

ABSTRACT

Methods, systems, and apparatus include computer programs encoded on a computer-readable storage medium, including a system that controls content distribution using a feedback loop. Content is distributed over multiple different online channels using a same initial selection value for distribution over each different online channel. An observed user actions required for distribution of the content over the multiple different online channels is received through a feedback loop and for multiple different distributions of the content. Based on the observed user actions received through the feedback loop, a predicted user action rate is determined for the multiple different distributions across the multiple different online channels. The selection value is adjusted based on a difference between the predicted user action rate and a reference distribution amount specified by a provider of the content. The content is distributed over the multiple different online channels using the adjusted selection value.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation application of U.S. application Ser.No. 16/281,538, filed Feb. 21, 2019, which is a continuation applicationof U.S. application Ser. No. 16/015,873, filed Jun. 22, 2018, which is acontinuation application of U.S. application Ser. No. 15/290,940, filedOct. 11, 2016, which are incorporated by reference.

BACKGROUND

This specification relates to optimizing a multi-channel system using afeedback loop.

The Internet provides access to a wide variety of resources. Forexample, video and/or audio files, as well as webpages for particularsubjects or particular news articles, are accessible over the Internet.Access to these resources presents opportunities for third-party contentto be provided with the resources. For example, a webpage can includeslots in which content can be presented.

SUMMARY

In general, one aspect of the subject matter described in thisspecification can be implemented in systems that include one or moreprocessing devices and one or more storage devices. One system includesa third-party content corpus database that stores information forvarious content that are available to be distributed to client devices.The system also includes a distributed computing environment, includingmultiple computing devices and a feedback loop. The distributedcomputing environment interacts with the third-party content corpusdatabase and performs operations. The operations include: distributingcontent over multiple different online channels using a same initialmaximum selection value for distribution over each different onlinechannel; receiving, through the feedback loop and for multiple differentdistributions of the content, an observed distribution amount requiredfor distribution of the content over the multiple different onlinechannels; determining, based on the observed distribution amountreceived through the feedback loop, a predicted user action rate for themultiple different distributions across the multiple different onlinechannels; adjusting the maximum selection value based on a differencebetween the predicted user action rate and a reference distributionamount specified by a provider of the content; and distributing thecontent over the multiple different online channels using the adjustedmaximum selection value.

These and other implementations can each optionally include one or moreof the following features. Adjusting the maximum selection value caninclude determining a difference between the predicted user action rateand the reference distribution amount; and adjusting the maximumselection value in proportion to the difference between the predicteduser action rate and the reference distribution amount, includingincreasing the maximum selection value in proportion to the differencewhen the predicted user action rate is less than the referencedistribution amount and decreasing the maximum selection value inproportion to the difference when the predicted user action rate isgreater than the reference distribution amount. Adjusting the maximumselection value can include adjusting the maximum selection value by acapped adjustment amount when the difference between the predicted useraction rate and the reference distribution amount is greater than aspecified threshold difference. Determining the predicted user actionrate can include determining an average distribution amount fordistributing the content over the multiple different online channelsbased on the observed distribution amount required for each distributionof the content over the multiple different online channels. Determiningthe average distribution amount can include determining, for a givenperiod of time, a ratio of a total observed distribution amount to apredicted user action rate for each distribution of the content duringthe given period of time, the total observed distribution amount being atotal amount required to distribute the content over the given period oftime. The distributed computing environment can perform operations thatfurther include generating the initial maximum selection value based onthe reference distribution amount and an initialization factor thatcauses the initial maximum selection value to be greater than thereference distribution amount. Distributing the content over themultiple different online channels can include distributing the contentover each of a search engine channel and a streaming media channel.

In general, another innovative aspect of the subject matter described inthis specification can be implemented in methods that include a methodfor distributing content. The method includes: distributing, by one ormore servers, content over multiple different online channels using asame initial maximum selection value for distribution over eachdifferent online channel; receiving, through a feedback loop and formultiple different distributions of the content, an observeddistribution amount required for distribution of the content over themultiple different online channels; determining, by the one or moreservers and based on the observed distribution amount received throughthe feedback loop, a predicted user action rate for the multipledifferent distributions across the multiple different online channels;adjusting, by the one or more servers, the maximum selection value basedon a difference between the predicted user action rate and a referencedistribution amount specified by a provider of the content; anddistributing, by the one or more servers, the content over the multipledifferent online channels using the adjusted maximum selection value.

These and other implementations can each optionally include one or moreof the following features. Adjusting the maximum selection value caninclude determining a difference between the predicted user action rateand the reference distribution amount; and adjusting the maximumselection value in proportion to the difference between the predicteduser action rate and the reference distribution amount, includingincreasing the maximum selection value in proportion to the differencewhen the predicted user action rate is less than the referencedistribution amount and decreasing the maximum selection value inproportion to the difference when the predicted user action rate isgreater than the reference distribution amount. Adjusting the maximumselection value can include adjusting the maximum selection value by acapped adjustment amount when the difference between the predicted useraction rate and the reference distribution amount is greater than aspecified threshold difference. Determining the predicted user actionrate can include determining an average distribution amount fordistributing the content over the multiple different online channelsbased on the observed distribution amount required for each distributionof the content over the multiple different online channels. Determiningthe average distribution amount can include determining, for a givenperiod of time, a ratio of a total observed distribution amount to apredicted user action rate for each distribution of the content duringthe given period of time, the total observed distribution amount being atotal amount required to distribute the content over the given period oftime. The method can further include generating the initial maximumselection value based on the reference distribution amount and aninitialization factor that causes the initial maximum selection value tobe greater than the reference distribution amount. Distributing thecontent over the multiple different online channels can includedistributing the content over each of a search engine channel and astreaming media channel.

In general, another aspect of the subject matter described in thisspecification can be implemented a non-transitory computer storagemedium encoded with a computer program. The program can includeinstructions that when executed by a distributed computing system causethe distributed computing system to perform operations includingdistributing, by one or more servers, content over multiple differentonline channels using a same initial maximum selection value fordistribution over each different online channel; receiving, through afeedback loop and for multiple different distributions of the content,an observed distribution amount required for distribution of the contentover the multiple different online channels; determining, by the one ormore servers and based on the observed distribution amount receivedthrough the feedback loop, a predicted user action rate for the multipledifferent distributions across the multiple different online channels;adjusting, by the one or more servers, the maximum selection value basedon a difference between the predicted user action rate and a referencedistribution amount specified by a provider of the content; anddistributing, by the one or more servers, the content over the multipledifferent online channels using the adjusted maximum selection value.

These and other implementations can each optionally include one or moreof the following features. Adjusting the maximum selection value caninclude determining a difference between the predicted user action rateand the reference distribution amount; and adjusting the maximumselection value in proportion to the difference between the predicteduser action rate and the reference distribution amount, includingincreasing the maximum selection value in proportion to the differencewhen the predicted user action rate is less than the referencedistribution amount and decreasing the maximum selection value inproportion to the difference when the predicted user action rate isgreater than the reference distribution amount. Adjusting the maximumselection value can include adjusting the maximum selection value by acapped adjustment amount when the difference between the predicted useraction rate and the reference distribution amount is greater than aspecified threshold difference. Determining the predicted user actionrate can include determining an average distribution amount fordistributing the content over the multiple different online channelsbased on the observed distribution amount required for each distributionof the content over the multiple different online channels. Determiningthe average distribution amount can include determining, for a givenperiod of time, a ratio of a total observed distribution amount to apredicted user action rate for each distribution of the content duringthe given period of time, the total observed distribution amount being atotal amount required to distribute the content over the given period oftime. The operations can further include generating the initial maximumselection value based on the reference distribution amount and aninitialization factor that causes the initial maximum selection value tobe greater than the reference distribution amount.

Implementations of the subject matter described in this specificationcan provide one or more of the following advantages. A maximum selectionvalue that is used to transmit third-party content over multipledifferent transmission channels can be dynamically modified over time(e.g., in real-time or near real-time) so that the aggregatedistribution amount required to transmit the third-party content overthe multiple different transmission channels meets a referencedistribution amount. The modification of the maximum selection valuealso ensures that the third-party content is distributed efficientlyacross all of the multiple different transmission channels even when thedifferent transmission channels are unrelated or independent from eachother. In some implementations, the modifications to the maximumselection value are made more quickly by using a realized distributionvalue is expected to be observed rather than waiting a longer period oftime to use historical distribution value information. This alsoprevents delays resulting from information that will only be obtained ata future time (e.g., conversion information) from negatively impactingthe ability to modify the maximum selection value or delayingmodification of the maximum selection value.

The details of one or more implementations of the subject matterdescribed in this specification are set forth in the accompanyingdrawings and the description below. Other features, aspects, andadvantages of the subject matter will become apparent from thedescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in which third-partycontent is distributed for presentation with electronic documents.

FIG. 2 is a flowchart of an example process for using a feedback loop tocontrol the distribution of content.

FIG. 3 shows an example system for using a feedback loop to control thedistribution of content.

FIG. 4 is a block diagram of an example computer system that can be usedto implement the methods, systems and processes described in thisdisclosure.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Systems, methods, and computer program products are described fordynamically modifying transmission channels using a feedback loop. Forexample, the feedback loop can be used to modify transmission ofthird-party content that is being transmitted based on a single set ofchannel independent values (e.g., that are used to transmit thethird-party content across the multiple different online channels). Thevalues can include or be limited to a total planned maximum distributionamount and a reference distribution amount, both values specified asingle time, such as when a content selection plan is defined by acontent provider. The content provider does need not to specify onlinechannel-specific values or be concerned with the differences of contentselection and resulting user actions for each channel. For example,based on content provider inputs, an initial maximum selection value canbe set (e.g., in the content selection plan) for the distribution ofcontent over each of the different online channels. Using the feedbackloop and after multiple different distributions of the content, anobserved distribution amount required for distribution of the contentover the multiple different online channels can be received ordetermined. Based on the observed distribution amount received throughthe feedback loop, a predicted user action rate can be determined forthe multiple different distributions across the multiple differentonline channels. The maximum selection value can be adjusted (e.g., fromthe initial value or from the currently set value) based on a differencebetween the predicted user action rate and a reference distributionamount specified by a provider of the content. Using the adjustedmaximum selection value, the distribution of the content over themultiple different online channels can continue.

FIG. 1 is a block diagram of an example environment 100 in whichthird-party content is distributed for presentation with electronicdocuments. The example environment 100 includes a network 102, such as alocal area network (LAN), a wide area network (WAN), the Internet, or acombination thereof. The network 102 connects electronic documentservers 104, user devices 106, third-party content servers 108, and athird-party content distribution system 110 (also referred to as acontent distribution system). The example environment 100 may includemany different electronic document servers 104, user devices 106, andthird-party content servers 108. In some implementations, theenvironment 100 includes multiple different online channels 124, e.g.,search engine channels, streaming media channel, and other channelsthrough which content can be distributed.

A user device 106 is an electronic device that is capable of requestingand receiving resources (e.g., electronic documents) over the network102. Example user devices 106 include personal computers, mobilecommunication devices, and other devices that can send and receive dataover the network 102. A user device 106 typically includes a userapplication, such as a web browser, to facilitate the sending andreceiving of data over the network 102, but native applications executedby the user device 106 can also facilitate the sending and receiving ofdata over the network 102.

An electronic document is data that presents a set of content at a userdevice 106. Examples of electronic documents include webpages, wordprocessing documents, portable document format (PDF) documents, images,videos, search results pages, and feed sources. Native applications(e.g., “apps”), such as applications installed on mobile, tablet, ordesktop computing devices are also examples of electronic documents.Electronic documents can be provided to user devices 106 by electronicdocument servers 104. For example, the electronic document servers 104can include servers that host publisher websites. In this example, theuser device 106 can initiate a request for a given publisher webpage,and the electronic document server 104 that hosts the given publisherwebpage can respond to the request by sending machine Hyper-Text MarkupLanguage (HTML) code that initiates presentation of the given webpage atthe user device 106.

In another example, the electronic document servers 104 can include appservers from which user devices 106 can download apps. In this example,the user device 106 can download files required to install an app at theuser device 106, and then execute the downloaded app locally.

Electronic documents can include a variety of content. For example,electronic document can include static content (e.g., text or otherspecified content) that is within the electronic document itself and/ordoes not change over time. Electronic documents can also include dynamiccontent that may change over time or on a per-request basis. Forexample, a publisher of a given electronic document can maintain a datasource that is used to populate portions of the electronic document. Inthis example, the given electronic document can include a tag or scriptthat causes the user device 106 to request content from the data sourcewhen the given electronic document is processed (e.g., rendered orexecuted) by a user device 106. The user device 106 integrates thecontent obtained from the data source into a presentation of the givenelectronic document to create a composite electronic document includingthe content obtained from the data source.

In some situations, a given electronic document can include athird-party tag or third-party script that references the third-partycontent distribution system 110. In these situations, the third-partytag or third-party script is executed by the user device 106 when thegiven electronic document is processed by the user device 106. Executionof the third-party tag or third-party script configures the user device106 to generate a request 112 for third-party content, which istransmitted over the network 102 to the third-party content distributionsystem 110. For example, the third-party tag or third-party script canenable the user device 106 to generate packetized data request includinga header and payload data. The request 112 can include data such as aname (or network location) of a server from which the third-partycontent is being requested, a name (or network location) of therequesting device (e.g., the user device 106), and/or information thatthe third-party content distribution system 110 can use to selectthird-party content provided in response to the request. The request 112is transmitted, by the user device 106, over the network 102 (e.g., atelecommunications network) to a server of the third-party contentdistribution system 110.

The request 112 can include data specifying the electronic document andcharacteristics of locations at which third-party content can bepresented. For example, data specifying a reference (e.g., URL) to anelectronic document (e.g., webpage) in which the third-party contentwill be presented, available locations (e.g., third-party content slots)of the electronic documents that are available to present third-partycontent, sizes of the available locations, positions of the availablelocations within a presentation of the electronic document, and/or mediatypes that are eligible for presentation in the locations can beprovided to the third-party content distribution system 110. Similarly,data specifying keywords designated for the selection of the electronicdocument (“document keywords”) or entities (e.g., people, places, orthings) that are referenced by the electronic document can also beincluded in the request 112 (e.g., as payload data) and provided to thethird-party content distribution system 110 to facilitate identificationof third-party content items that are eligible for presentation with theelectronic document.

Requests 112 can also include data related to other information, such asinformation that the user has provided, geographic informationindicating a state or region from which the request was submitted, orother information that provides context for the environment in which thethird-party content will be displayed (e.g., a type of device at whichthe third-party content will be displayed, such as a mobile device ortablet device). Data specifying characteristics of the user device 106can also be provided in the request 112, such as information thatidentifies a model of the user device 106, a configuration of the userdevice 106, or a size (e.g., physical size or resolution) of anelectronic display (e.g., touchscreen or desktop monitor) on which theelectronic document is presented. Requests 112 can be transmitted, forexample, over a packetized network, and the requests 112 themselves canbe formatted as packetized data having a header and payload data. Theheader can specify a destination of the packet and the payload data caninclude any of the information discussed above.

The third-party content distribution system 110 selects third-partycontent (e.g., information about apps) that will be presented with thegiven electronic document in response to receiving the request 112and/or using information included in the request 112. In someimplementations, the third-party content is selected in less than asecond to avoid errors that could be caused by delayed selection of thethird-party content. For example, delays in providing third-partycontent in response to a request 112 can result in page load errors atthe user device 106 or cause portions of the electronic document toremain unpopulated even after other portions of the electronic documentare presented at the user device 106. Also, as the delay in providingthird-party content to the user device 106 increases, it is more likelythat the electronic document will no longer be presented at the userdevice 106 when the third-party content, thereby negatively impacting auser's experience with the electronic document. Further, delays inproviding the third-party content can result in a failed delivery of thethird-party content, for example, if the electronic document is nolonger presented at the user device 106 when the third-party content isprovided.

In some implementations, the third-party content distribution system 110is implemented in a distributed computing system (or environment) thatincludes, for example, a server and a set of multiple computing devices114 that are interconnected and identify and distribute third-partycontent in response to requests 112. The set of multiple computingdevices 114 operate together to identify a set of third-party contentthat are eligible to be presented in the electronic document from amonga corpus of millions of available third-party content (3PC1-x). Themillions of available third-party content can be indexed, for example,in a third-party corpus database 116. Each third-party content indexentry can reference the corresponding third-party content and/or includedistribution parameters (DP1-DPx) (e.g. selection criteria) thatcondition the distribution of the corresponding third-party content.

In some implementations, the distribution parameters (e.g., selectioncriteria) for a particular third-party content can include distributionkeywords that must be matched (e.g., by electronic documents or termsspecified in the request 112) in order for the third-party content to beeligible for presentation. The distribution parameters can also requirethat the request 112 include information specifying a particulargeographic region (e.g., country or state) and/or information specifyingthat the request 112 originated at a particular type of user device(e.g., mobile device or tablet device) in order for the third-partycontent to be eligible for presentation.

The identification of the eligible third-party content can be segmentedinto multiple tasks 117 a-117 c that are then assigned among computingdevices within the set of multiple computing devices 114. For example,different computing devices in the set of multiple computing devices 114can each analyze a different portion of the third-party corpus database116 to identify various third-party content having distributionparameters that match information included in the request 112. In someimplementations, each given computing device in the set of multiplecomputing devices 114 can analyze a different data dimension (or set ofdimensions) and pass results 118 a-118 c of the analysis back to thethird-party content distribution system 110. For example, the results118 a-118 c provided by each of the computing devices in the set mayidentify a subset of third-party content that are eligible fordistribution in response to the request and/or a subset of thethird-party content that have certain distribution parameters orattributes.

The third-party content distribution system 110 aggregates the results118 a-118 c received from the set of multiple computing devices 114 anduses information associated with the aggregated results to select one ormore instances of third-party content that will be provided in responseto the request 112. For example, the third-party content distributionsystem 110 can select a set of winning third-party content based on theoutcome of one or more content evaluation processes, as discussed infurther detail below. In turn, the third-party content distributionsystem 110 can generate and transmit, over the network 102, reply data120 (e.g., digital data representing a reply) that enable the userdevice 106 to integrate the set of winning third-party content into thegiven electronic document, such that the set of winning third-partycontent and the content of the electronic document are presentedtogether at a display of the user device 106.

In some implementations, the user device 106 executes instructionsincluded in the reply data 120, which configures and enables the userdevice 106 to obtain the set of winning third-party content from one ormore third-party content servers. For example, the instructions in thereply data 120 can include a network location (e.g., a Uniform ResourceLocator (URL)) and a script that causes the user device 106 to transmita third-party request 121 to the third-party content server 108 toobtain a given winning third-party content from the third-party contentserver 108. In response to the request, the third-party content server108 will transmit, to the user device 106, third-party data 122 thatcauses the given winning third-party content to be incorporated into theelectronic document and presented at the user device 106.

The third-party content distribution system 110 can utilize one or moreevaluation processes to identify and select the set of winningthird-party content for each given request (e.g., based on datacorresponding to the request). In some implementations, the evaluationprocess is not only required to determine which third-party content toselect for presentation with the electronic document, but also the typeof formatting that will be dynamically (e.g., on a per-request basis)applied to the selected third-party content, and the price that will bepaid for presentation of the selected third-party content when presentedwith the applied formatting.

In some implementations, the third-party content distribution system 110may select winning third-party content in response to a given requestfrom among a set of third-party content items (e.g., instances ofthird-party content) that have been deemed eligible to return to theuser device 106 in response to that request. Eligible third-partycontent can be identified on a per-request basis according to variousdata specified in the request, or context associated with the request.For example, the request may indicate a permissible size or aspect ratioof the requested third-party content, and the third-party contentdistribution system 110 may filter the total set of availablethird-party content to a set of eligible content that satisfies the sizeor aspect ratio constraint, and any other applicable constraints. Theevaluation process for selecting winning third-party content can then beperformed only with respect to the set of eligible third-party content.In some examples, the evaluation process may involve scoring and rankingthird-party content items. The winning third-party content item may bethe highest-ranked item according to the scores. In someimplementations, the ineligible third-party content may be excluded fromthe total set of available content before the content is scored andranked. In some implementations, the ineligible third-party content maybe excluded from the total set of available content after the content isscored and ranked.

As described further with respect to FIGS. 2-3, the third-party contentdistribution system 110 can control content distribution using afeedback loop. In some implementations, a maximum selection value (e.g.,a maximum cost-per-action “CPA” bid) can be changed automatically basedon information received through the feedback loop. For example, based onan observed distribution amount (e.g., a total cost of distributingthird-party content for a given content provider), a realizeddistribution amount (e.g., an expected CPA for distributing thethird-party content over a specified period of time), and a referencedistribution amount (e.g., a target CPA specified by a contentprovider).

For purposes of example, assume that a content provider specifies areference distribution amount of 5.0 for distribution of theirthird-party content. The reference distribution amount can be an averagecontent distribution amount, such as a target cost-per-action, that isspecified by the content sponsor initially and/or adjusted over time.Although the reference distribution amount is specified to be 5.0, thethird-party content distribution system 110 may use a higher amount,such as 6.0, as the initial maximum selection value (e.g., bid or othervalue used for purposes of selecting third-party content that will bedistributed) in an attempt to achieve the reference distribution amountof 5.0.

The content distribution system 110 can automatically adjust the maximumselection value over time based on a difference between the realizeddistribution amount for the content provider and the referencedistribution amount specified by the content provider. For example,using information provided through the feedback loop at one point intime, the third-party content distribution system 110 can determine thatthe expected distribution amount (e.g., observed distributionamount/predicted user action rate) is less than the referencedistribution amount, and in response to the determination, increase themaximum selection value to a higher level, such as 7.0. The increasedmaximum selection value will cause the expected distribution amount toincrease toward the reference distribution amount of 5.0, as discussedin more detail below. Similarly, when the expected distribution amountis determined to be greater than the reference distribution amount(e.g., based on the information provided through the feedback loop at adifferent point in time), the third-party content distribution system110 can decrease the maximum selection value to a lower level, such as4.0 (or some other value). The decreased maximum selection value willcause the expected distribution amount to decrease toward the referencedistribution amount. Thus, using the information provided through thefeedback loop, the third-party content distribution system 110 cancontinually, automatically, and iteratively adjust the selection valueto more accurately achieve the reference distribution amount specifiedby the content provider, even where the third-party content is beingdistributed over various different kinds of channels at variousdifferent levels of costs per action.

In some implementations, the content distribution system 110 canautomatically adjust the maximum selection value over time based on acurrent difference between a maximum spend amount specified by thecontent provider and a planned maximum spend amount (e.g., maximumbudget) specified by the content provider. The difference, for example,can be a difference between a real-time indicator of a goal spend amount(e.g., 1000.0) that the content provider has specified versus anabsolute maximum (e.g., 1200.0) that the content provider has specifiedcan be spent. Over time, if the goal spend amount has been reached, orif the goal amount is predicted (e.g., based on content that has beenprovided) to exceed the goal spend amount during a time period specifiedfor the selection of the content provider's content, then the maximumselection value can be adjusted downward as part of the feedback loop.Doing so can avoid the situation in which the planned maximum spendamount is reached (or is predicted to be reached too fast), or theabsolute maximum is reached at all. The change in the adjustment themaximum selection value can also depend on other criteria, such as acurrent difference between the realized distribution amount for thecontent provider and the reference distribution amount specified by thecontent provider, as described above. Additionally, if the goal spendamount has not been reached (or is not predicted to be reached within atime period specified for providing the content provider's content),then the maximum selection value can be increased as part of thefeedback loop.

FIG. 2 is a flowchart of an example process 200 for optimizing amulti-channel system using a feedback loop. The optimization caninclude, for example, identifying a single maximum selection value(e.g., a single ranking score, CPA bid, or another selection value) touse for distribution of third-party content provided by a particularcontent provider across the multiple different channels of the system.In some implementations, the third-party content distribution system 110and components that it includes can perform operations of the process200. In some implementations, operations of the process 200 can beimplemented by one or more servers and a memory device storinginstructions that are executed by one or more servers. Operations of theprocess 200 can also be implemented using computer-readable instructionsthat are stored on a non-transitory computer readable medium, such thatwhen the instructions are executed by one or more computing devices(e.g., servers), the instructions cause the one or more computingdevices to perform operations of the process 200. The process 200 isdescribed with reference to FIGS. 1 and 3 in order to identify examplestructures for performing the operations of the process 200. Forexample, stages 1-5 in FIG. 3 (discussed below) correspond to operations202-210, respectively.

FIG. 3 shows an example system 300 for optimizing a multi-channel systemusing a feedback loop 302. In some implementations, the third-partycontent distribution system 110 can use information obtained and/orprocessed in the feedback loop 302 to manage distribution of third-partycontent (e.g., content 304 a, 304 b) through the multiple differentonline channels 124 using a same selection value across all of thechannels. For example, as third-party content is distributed though themultiple channels, user action information 312 specifying user actions(e.g., conversions such as purchases, file/application downloads, e-maillist sign-ups, etc.) that are attributed to presentation of thethird-party content is fed through the feedback loop 302, and used tomake adjustments to the same selection value that is used to controldistribution of content through all of the various transmissionchannels.

The user action information 312 can identify, for example, theparticular one of the multiple different online channels 124 (e.g.,search engines, publisher web pages, application stores, video streamingservices, audio streaming services, etc.) on which a particular useraction occurred. The user action information 312 can also identifyspecific users or categories of users that performed the user actions,dates and times of the user actions, device types (e.g., mobile versusnon-mobile) through which the user actions were performed, geographicand/or location information where the user actions occurred, and/orother information that can about a specific user action that isattributable to a specific presentation of content. Although thefeedback loop 302 is shown as including elements 306-310, the elementsof the feedback loop 302 can be different as needed to control contentdistribution in an efficient way, e.g., to improve the transmission ofcontent over the multiple different online channels 124 using a sameselection value.

Referring again to FIG. 2 and the process 200, content is distributedover multiple different online channels using a same initial maximumselection value for distribution over each different online channel(202). In some implementations, the third-party content distributionsystem 110 can distribute (e.g., at stage 1 in FIG. 3) content 304 ausing initial selection values. Selection values can be any quantitativevalue (e.g., a score, a weight, a bid, etc.) that is used to determinewhich third-party content will be selected for distribution at any givenpoint in time. For example, selection values for various third-partycontent can be obtained from a data store and compared to rank thevarious third-party content (e.g., in descending order of selectionvalues). The highest ranked third-party content will generally beselected for distribution.

In some implementations, the same initial maximum selection value (e.g.,max CPA bid) used to distribute third-party content for a given contentprovider can be generated, for example, based on the referencedistribution amount (e.g., target CPA amount) that has been specified bythe given content provider and an initialization factor that causes theinitial maximum selection value to be greater than the referencedistribution amount. In some implementations, the initialization factorcan be determined by analyzing historical prices across the variouschannels and/or in other ways. For example, historical distributionamounts required to distribute content on video streaming services maybe higher, on average, than historical distribution amounts required todistribute content on other online channels, and historical distributionamounts required to distribute content on search engines may be lower,on average, than historical distribution amounts required to distributecontent on the other online channels.

Using information regarding the variance of historical distributionamounts required to distribute content on the various channels incombination with other factors (e.g., a distribution percentage ofcontent provided on each of the various channels), the initializationfactor can be selected for use in setting the initial maximum selectionvalue. An initialization factor of 120% (also expressed as 1.2), forexample, can cause the initial maximum selection value (e.g., 6.0) to be20% higher than the reference distribution amount (e.g., 5.0) specifiedby the given content provider. In this way, at least during an initialperiod in which the given content provider's content is selected fordistribution, the maximum selection value will be set higher than thereference distribution amount. This difference between the maximumselection value and the reference distribution amount can continue, forexample, until changed through a feedback loop.

An observed distribution amount (e.g., observed/actual CPA amount)required for distribution of the content that has occurred over themultiple different online channels is received through the feedback loopand for multiple different distributions of the content (204). In someimplementations, the observed distribution amount 306 can be determinedby the system as the system tracks the amount the content provider hassubmitted so far for the distribution of the content over the variouschannels. This can occur, for example, after the system tracks (andstores over time) an amount submitted by a given content provider fordistribution of their content over the various channels.

Based on the observed distribution amount received through the feedbackloop, a realized distribution amount is determined for the multipledifferent distributions across the multiple different online channels(206). For example, based on information identifying and quantifying theresults of (e.g., user interactions with) the distribution of at leastcontent 204 a that is distributed by the third-party contentdistribution system 110, the third-party content distribution system 110can determine (e.g., at stage 3 in FIG. 3) a realized distributionamount 308 (e.g., an expected CPA). For example, the realizeddistribution amount can be determined as a function of observeddistribution amount divided by a predicted user action rate (e.g.,conversion rate), such as based on a cost of providing the contentrelative to a number of specified user actions with the content (e.g.,post-click conversions). The realized distribution amount (e.g., 6.0)can identify, e.g., based on the function, an expected cost-per-actionthat will be realized using the current cost-per-action bid (i.e.,selection value).

In some implementations, determining the realized distribution amountcan include determining an average distribution amount for distributingthe content over the multiple different online channels based on theobserved distribution amount required for each distribution of thecontent over the multiple different online channels. For example, thethird-party content distribution system 110 can determine differentdistribution amounts for each of the multiple different online channels124. Using the different distribution amounts, for example, thethird-party content distribution system 110 can calculate an averagedistribution amount (e.g., an average CPA) across all of the multipledifferent online channels 124. In some implementations, the averagedistribution amount can be determined using, for example, a weightedaverage, such as by assigning a greater weight to channels based onvolume of distributed content.

In some implementations, determining the average distribution amount caninclude determining, for a given period of time, a ratio of a totalobserved distribution amount to a predicted user action rate for eachdistribution of the content during the given period of time, the totalobserved distribution amount being a total amount required to distributethe content over the given period of time. For example, the third-partycontent distribution system 110 can compute the realized distributionamount as a function of total spend divided by pCVR.

The maximum selection value is adjusted based on a difference betweenthe realized distribution amount and a reference distribution amountspecified by a provider of the content (208). For example, based oncost-per-action determinations and the observed distribution amountreceived through the feedback loop 302, the third-party contentdistribution system 110 can adjust (e.g., at stage 4 in FIG. 3) themaximum cost-per-action, creating an adjusted maximum selection value310. The adjustment can occur, for example, whenever the current valueof the realized distribution amount differs (e.g., by a threshold amountor by a threshold percentage) from the reference distribution amountspecified by content provider. As an example, the maximumcost-per-action can be adjusted up to 7.0. The maximum cost-per-actioncan be adjusted iteratively, over time.

In some implementations, adjusting the maximum selection value caninclude determining a difference between the realized distributionamount and the reference distribution amount, and the maximum selectionvalue can be adjusted in proportion to the difference between therealized distribution amount and the reference distribution amount. Theadjusting can include increasing the maximum selection value inproportion to the difference when the realized distribution amount isless than the reference distribution amount. The adjusting can alsoinclude decreasing the maximum selection value in proportion to thedifference when the realized distribution amount is greater than thereference distribution amount. For example, the third-party contentdistribution system 110 can adjust (e.g., either by increasing ordecreasing) the maximum cost-per-action selection value in proportion toa difference between the realized distribution amount and the referencedistribution amount. The adjustment can occur, for example, when thethird-party content distribution system 110 determines that thedifference between the realized distribution amount and the referencedistribution amount exceeds a given amount or a given percentage. Insome implementations, given amounts and/or given percentages can bespecific to one, some, or all content providers.

In some implementations, adjusting the maximum selection value caninclude adjusting the maximum selection value by a capped adjustmentamount when the difference between the realized distribution amount andthe reference distribution amount is greater than a specified thresholddifference. For example, the third-party content distribution system 110can adjust the maximum cost-per-action selection value by an amount thatis below a pre-determined value, or by a value that is below a maximumpercentage of the currently-set maximum cost-per-action. Using a cappedadjustment amount, for example, can provide stability of the system 300by preventing large swings in selection values.

The content is distributed over the multiple different online channelsusing the adjusted maximum selection value (210). For example, using anew maximum selection value of 7.0, the third-party content distributionsystem 110 can distribute (e.g., at stage 5 in FIG. 3) content 304 b tothe multiple different online channels 124 (e.g., search enginechannels, streaming media channel, and other channels) using the newmaximum selection value.

In some implementations, the process 200 can further include generatingthe initial maximum selection value based on the reference distributionamount and an initialization factor that causes the initial maximumselection value to be greater than the reference distribution amount. Asan example, the third-party content distribution system 110 caninitialize the maximum cost-per-action as a function of referencedistribution amount times a constant.

The feedback loop 302 can typically operate autonomously, e.g., withoutperiodic intervention from the content provider. In someimplementations, the feedback loop 302 can include one or more aspectsof notifying the content provider of issues, and receiving andprocessing inputs from the content provider. For example, determinationsand calculations made in the feedback loop 302 may indicate that thereis a problem with the content selection plan or with the contentprovider's initial inputs (e.g., total planned maximum distributionamount and maximum cost-per-action). As such, the process 200 canfurther include additional operations, e.g., a notification-relatedoperations and one or more updating/processing operations (e.g., thatuse the content provider's inputs). Other notifications can periodicallybe made to the content provider, e.g., to indicate how the contentselection plan is going, what the current maximum selection value is, orto provide statistical information about user actions that occurredsubsequent to the distribution of content.

In some implementations, statistics regarding the selection of contentand subsequent user actions can be gathered and/or calculated regularlyand used in the feedback loop 302. For example, a predicted averagecost-per-action and an average maximum cost-per-action, both of whichcan be, can be gathered from n days of logs. In some implementations,information used in the feedback loop 302 can be weighted based on howrecently user actions being reported were performed. For example, anumber of user actions that have occurred in the last 24 hours can beweighted higher than the same number of user actions that occurred overan earlier period.

In some implementations, calculations made in the feedback loop 302 caninclude the use of a predicted user action rate in addition toinformation about actual user actions. Using various types ofinformation can provide an advantage of factoring in delays that occurfor user actions after the presentation of content. In someimplementations, delays that occur (and that are considered and/orpredicted) can be different for the multiple different online channels124.

FIG. 4 is a block diagram of example computing devices 400, 450 that canbe used to implement the systems and methods described in this document,as either a client or as a server or plurality of servers. Computingdevice 400 is intended to represent various forms of digital computers,such as laptops, desktops, workstations, personal digital assistants,servers, blade servers, mainframes, and other appropriate computers.Computing device 400 is further intended to represent any othertypically non-mobile devices, such as televisions or other electronicdevices with one or more processers embedded therein or attachedthereto. Computing device 450 is intended to represent various forms ofmobile devices, such as personal digital assistants, cellulartelephones, smartphones, and other computing devices. The componentsshown here, their connections and relationships, and their functions,are meant to be examples only, and are not meant to limitimplementations of the inventions described and/or claimed in thisdocument.

Computing device 400 includes a processor 402, memory 404, a storagedevice 406, a high-speed controller 408 connecting to memory 404 andhigh-speed expansion ports 410, and a low-speed controller 412connecting to low-speed bus 414 and storage device 406. Each of thecomponents 402, 404, 406, 408, 410, and 412, are interconnected usingvarious busses, and may be mounted on a common motherboard or in othermanners as appropriate. The processor 402 can process instructions forexecution within the computing device 400, including instructions storedin the memory 404 or on the storage device 406 to display graphicalinformation for a GUI on an external input/output device, such asdisplay 416 coupled to high-speed controller 408. In otherimplementations, multiple processors and/or multiple buses may be used,as appropriate, along with multiple memories and types of memory. Also,multiple computing devices 400 may be connected, with each deviceproviding portions of the necessary operations (e.g., as a server bank,a group of blade servers, or a multi-processor system).

The memory 404 stores information within the computing device 400. Inone implementation, the memory 404 is a computer-readable medium. In oneimplementation, the memory 404 is a volatile memory unit or units. Inanother implementation, the memory 404 is a non-volatile memory unit orunits.

The storage device 406 is capable of providing mass storage for thecomputing device 400. In one implementation, the storage device 406 is acomputer-readable medium. In various different implementations, thestorage device 406 may be a floppy disk device, a hard disk device, anoptical disk device, or a tape device, a flash memory or other similarsolid state memory device, or an array of devices, including devices ina storage area network or other configurations. In one implementation, acomputer program product is tangibly embodied in an information carrier.The computer program product contains instructions that, when executed,perform one or more methods, such as those described above. Theinformation carrier is a computer- or machine-readable medium, such asthe memory 404, the storage device 406, or memory on processor 402.

The high-speed controller 408 manages bandwidth-intensive operations forthe computing device 400, while the low-speed controller 412 manageslower bandwidth-intensive operations. Such allocation of duties is anexample only. In one implementation, the high-speed controller 408 iscoupled to memory 404, display 416 (e.g., through a graphics processoror accelerator), and to high-speed expansion ports 410, which may acceptvarious expansion cards (not shown). In the implementation, low-speedcontroller 412 is coupled to storage device 406 and low-speed bus 414.The low-speed bus 414 (e.g., a low-speed expansion port), which mayinclude various communication ports (e.g., USB, Bluetooth®, Ethernet,wireless Ethernet), may be coupled to one or more input/output devices,such as a keyboard, a pointing device, a scanner, or a networking devicesuch as a switch or router, e.g., through a network adapter.

The computing device 400 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 420, or multiple times in a group of such servers. Itmay also be implemented as part of a rack server system 424. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 422. Alternatively, components from computing device 400 may becombined with other components in a mobile device (not shown), such ascomputing device 450. Each of such devices may contain one or more ofcomputing devices 400, 450, and an entire system may be made up ofmultiple computing devices 400, 450 communicating with each other.

Computing device 450 includes a processor 452, memory 464, aninput/output device such as a display 454, a communication interface466, and a transceiver 468, among other components. The computing device450 may also be provided with a storage device, such as a micro-drive orother device, to provide additional storage. Each of the components 450,452, 464, 454, 466, and 468, are interconnected using various buses, andseveral of the components may be mounted on a common motherboard or inother manners as appropriate.

The processor 452 can process instructions for execution within thecomputing device 450, including instructions stored in the memory 464.The processor may also include separate analog and digital processors.The processor may provide, for example, for coordination of the othercomponents of the computing device 450, such as control of userinterfaces, applications run by computing device 450, and wirelesscommunication by computing device 450.

Processor 452 may communicate with a user through control interface 458and display interface 456 coupled to a display 454. The display 454 maybe, for example, a TFT LCD display or an OLED display, or otherappropriate display technology. The display interface 456 may compriseappropriate circuitry for driving the display 454 to present graphicaland other information to a user. The control interface 458 may receivecommands from a user and convert them for submission to the processor452. In addition, an external interface 462 may be provided incommunication with processor 452, so as to enable near areacommunication of computing device 450 with other devices. Externalinterface 462 may provide, for example, for wired communication (e.g.,via a docking procedure) or for wireless communication (e.g., viaBluetooth® or other such technologies).

The memory 464 stores information within the computing device 450. Inone implementation, the memory 464 is a computer-readable medium. In oneimplementation, the memory 464 is a volatile memory unit or units. Inanother implementation, the memory 464 is a non-volatile memory unit orunits. Expansion memory 474 may also be provided and connected tocomputing device 450 through expansion interface 472, which may include,for example, a subscriber identification module (SIM) card interface.Such expansion memory 474 may provide extra storage space for computingdevice 450, or may also store applications or other information forcomputing device 450. Specifically, expansion memory 474 may includeinstructions to carry out or supplement the processes described above,and may include secure information also. Thus, for example, expansionmemory 474 may be provide as a security module for computing device 450,and may be programmed with instructions that permit secure use ofcomputing device 450. In addition, secure applications may be providedvia the SIM cards, along with additional information, such as placingidentifying information on the SIM card in a non-hackable manner.

The memory may include for example, flash memory and/or MRAM memory, asdiscussed below. In one implementation, a computer program product istangibly embodied in an information carrier. The computer programproduct contains instructions that, when executed, perform one or moremethods, such as those described above. The information carrier is acomputer- or machine-readable medium, such as the memory 464, expansionmemory 474, or memory on processor 452.

Computing device 450 may communicate wirelessly through communicationinterface 466, which may include digital signal processing circuitrywhere necessary. Communication interface 466 may provide forcommunications under various modes or protocols, such as GSM voicecalls, SMS, EMS, or MMS messaging, CDMA, TDMA, PDC, WCDMA, CDMA2000, orGPRS, among others. Such communication may occur, for example, throughtransceiver 468 (e.g., a radio-frequency transceiver). In addition,short-range communication may occur, such as using a Bluetooth®, WiFi,or other such transceiver (not shown). In addition, GPS receiver module470 may provide additional wireless data to computing device 450, whichmay be used as appropriate by applications running on computing device450.

Computing device 450 may also communicate audibly using audio codec 460,which may receive spoken information from a user and convert it tousable digital information. Audio codec 460 may likewise generateaudible sound for a user, such as through a speaker, e.g., in a handsetof computing device 450. Such sound may include sound from voicetelephone calls, may include recorded sound (e.g., voice messages, musicfiles, etc.) and may also include sound generated by applicationsoperating on computing device 450.

The computing device 450 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as acellular telephone 480. It may also be implemented as part of asmartphone 482, personal digital assistant, or other mobile device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. Other programming paradigms can be used, e.g., functionalprogramming, logical programming, or other programming. As used herein,the terms “machine-readable medium” “computer-readable medium” refers toany computer program product, apparatus and/or device (e.g., magneticdiscs, optical disks, memory, Programmable Logic Devices (PLDs)) used toprovide machine instructions and/or data to a programmable processor,including a machine-readable medium that receives machine instructionsas a machine-readable signal. The term “machine-readable signal” refersto any signal used to provide machine instructions and/or data to aprogrammable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (“LAN”), a wide area network (“WAN”), and theInternet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinventions or of what may be claimed, but rather as descriptions offeatures specific to particular implementations of particularinventions. Certain features that are described in this specification inthe context of separate implementations can also be implemented incombination in a single implementation. Conversely, various featuresthat are described in the context of a single implementation can also beimplemented in multiple implementations separately or in any suitablesubcombination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the implementations described above should not beunderstood as requiring such separation in all implementations, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

Thus, particular implementations of the subject matter have beendescribed. Other implementations are within the scope of the followingclaims. In some cases, the actions recited in the claims can beperformed in a different order and still achieve desirable results. Inaddition, the processes depicted in the accompanying figures do notnecessarily require the particular order shown, or sequential order, toachieve desirable results. In certain implementations, multitasking andparallel processing may be advantageous.

What is claimed is:
 1. A method, comprising: distributing, by one ormore servers in a distributed computing environment, content overmultiple different online channels using a same reference distributionamount specified by a provider of the content for distribution over themultiple different online channels, wherein a feedback loop isconfigured to obtain feedback about the distributions and adjust atransmission of content over the multiple different online channels byadjusting a selection value used to distribute content over the multipledifferent online channels; receiving, through a feedback loop and formultiple different distributions of the content over the multipledifferent online channels, observed user actions with the contentdistributed to client devices over the multiple different onlinechannels; determining, by the one or more servers and based on theobserved user actions received through the feedback loop, an observeddistribution amount for the multiple different distributions over themultiple different online channels; determining, based at least in parton the observed distribution amount and one or more additional criteria,that the selection value is eligible for adjustment for a currentdistribution of the content; adjusting, by the one or more servers, theselection value for the current distribution of content based on theobserved distribution amount the same reference distribution amountspecified by the provider of the content; and distributing, by the oneor more servers, the content using the adjusted selection value.
 2. Themethod of claim 1, further comprising: determining a difference betweena predicted user action rate and the same reference distribution amount;and adjusting the selection value in proportion to the differencebetween the predicted user action rate and the same referencedistribution amount, including increasing the selection value inproportion to the difference when the predicted user action rate is lessthan the reference distribution amount and decreasing the selectionvalue in proportion to the difference when the predicted user actionrate is greater than the reference distribution amount.
 3. The method ofclaim 2, wherein adjusting the selection value in proportion to thedifference between the predicted user action rate and the same referencedistribution amount comprises adjusting the selection value by a cappedadjustment amount when the difference between the predicted user actionrate and the same reference distribution amount is greater than aspecified threshold difference.
 4. The method of claim 2, whereindetermining the predicted user action rate includes determining anaverage distribution amount for distributing the content over themultiple different online channels based on the observed user actionsrequired for each distribution of the content over the multipledifferent online channels.
 5. The method of claim 4, wherein determiningthe average distribution amount comprises determining, for a givenperiod of time, a ratio of a total observed user actions to a predicteduser action rate for each distribution of the content during the givenperiod of time, the total observed user actions being a total amountrequired to distribute the content over the given period of time.
 6. Themethod of claim 1, further comprising generating an initial instance ofthe selection value based on the reference distribution amount and aninitialization factor that causes the initial instance of the selectionvalue to be greater than the reference distribution amount.
 7. Themethod of claim 1, wherein distributing the content over the multipledifferent online channels comprises distributing the content over eachof a web page and a streaming media channel.
 8. A system comprising: athird-party content corpus database that stores information for variouscontent that are available to be distributed to client devices; and adistributed computing environment, including multiple computing devicesand a feedback loop wherein the distributed computing environmentinteracts with the third-party content corpus database and performsoperations comprising: distributing, by one or more servers in adistributed computing environment, content over multiple differentonline channels using a same reference distribution amount specified bya provider of the content for distribution over the multiple differentonline channels, wherein a feedback loop is configured to obtainfeedback about the distributions and adjust a transmission of contentover the multiple different online channels by adjusting a selectionvalue used to distribute content over the multiple different onlinechannels; receiving, through a feedback loop and for multiple differentdistributions of the content over the multiple different onlinechannels, observed user actions with the content distributed to clientdevices over the multiple different online channels; determining, by theone or more servers and based on the observed user actions receivedthrough the feedback loop, an observed distribution amount for themultiple different distributions over the multiple different onlinechannels; determining, based at least in part on the observeddistribution amount and one or more additional criteria, that theselection value is eligible for adjustment for a current distribution ofthe content; adjusting, by the one or more servers, the selection valuefor the current distribution of content based on the observeddistribution amount the same reference distribution amount specified bythe provider of the content; and distributing, by the one or moreservers, the content using the adjusted selection value.
 9. The systemof claim 8, wherein the one or more servers in the distributed computingenvironment perform operations further comprising: determining adifference between a predicted user action rate and the same referencedistribution amount; and adjusting the selection value in proportion tothe difference between the predicted user action rate and the samereference distribution amount, including increasing the selection valuein proportion to the difference when the predicted user action rate isless than the reference distribution amount and decreasing the selectionvalue in proportion to the difference when the predicted user actionrate is greater than the reference distribution amount.
 10. The systemof claim 9, wherein adjusting the selection value in proportion to thedifference between the predicted user action rate and the same referencedistribution amount comprises adjusting the selection value by a cappedadjustment amount when the difference between the predicted user actionrate and the same reference distribution amount is greater than aspecified threshold difference.
 11. The system of claim 9, whereindetermining the predicted user action rate includes determining anaverage distribution amount for distributing the content over themultiple different online channels based on the observed user actionsrequired for each distribution of the content over the multipledifferent online channels.
 12. The system of claim 11, whereindetermining the average distribution amount comprises determining, for agiven period of time, a ratio of a total observed user actions to apredicted user action rate for each distribution of the content duringthe given period of time, the total observed user actions being a totalamount required to distribute the content over the given period of time.13. The system of claim 8, wherein the distributed computing environmentperforms operations further comprising generating an initial instance ofthe selection value based on the reference distribution amount and aninitialization factor that causes the initial instance of the selectionvalue to be greater than the reference distribution amount.
 14. Thesystem of claim 8, wherein distributing the content over the multipledifferent online channels comprises distributing the content over eachof a web page and a streaming media channel.
 15. A computer programproduct embodied in a non-transitory computer-readable medium includinginstructions, that when executed, cause one or more processors toperform operations comprising: distributing content over multipledifferent online channels using a same reference distribution amountspecified by a provider of the content for distribution over themultiple different online channels, wherein a feedback loop isconfigured to obtain feedback about the distributions and adjust atransmission of content over the multiple different online channels byadjusting a selection value used to distribute content over the multipledifferent online channels; receiving, through a feedback loop and formultiple different distributions of the content over the multipledifferent online channels, observed user actions with the contentdistributed to client devices over the multiple different onlinechannels; determining, based on the observed user actions receivedthrough the feedback loop, an observed distribution amount for themultiple different distributions over the multiple different onlinechannels; determining, based at least in part on the observeddistribution amount and one or more additional criteria, that theselection value is eligible for adjustment for a current distribution ofthe content; adjusting the selection value for the current distributionof content based on the observed distribution amount the same referencedistribution amount specified by the provider of the content; anddistributing the content using the adjusted selection value.
 16. Thecomputer program product of claim 15, wherein the instructions cause theone or more processors to perform operations further comprising:determining a difference between a predicted user action rate and thesame reference distribution amount; and adjusting the selection value inproportion to the difference between the predicted user action rate andthe same reference distribution amount, including increasing theselection value in proportion to the difference when the predicted useraction rate is less than the reference distribution amount anddecreasing the selection value in proportion to the difference when thepredicted user action rate is greater than the reference distributionamount.
 17. The computer program product of claim 16, wherein adjustingthe selection value in proportion to the difference between thepredicted user action rate and the same reference distribution amountcomprises adjusting the selection value by a capped adjustment amountwhen the difference between the predicted user action rate and the samereference distribution amount is greater than a specified thresholddifference.
 18. The computer program product of claim 16, whereindetermining the predicted user action rate includes determining anaverage distribution amount for distributing the content over themultiple different online channels based on the observed user actionsrequired for each distribution of the content over the multipledifferent online channels.
 19. The computer program product of claim 18,wherein determining the average distribution amount comprisesdetermining, for a given period of time, a ratio of a total observeduser actions to a predicted user action rate for each distribution ofthe content during the given period of time, the total observed useractions being a total amount required to distribute the content over thegiven period of time.
 20. The computer program product of claim 15,wherein the instructions cause the one or more processors to performoperations further comprising generating an initial instance of theselection value based on the reference distribution amount and aninitialization factor that causes the initial instance of the selectionvalue to be greater than the reference distribution amount.